In this paper, we report on our ongoing research in the area of aeroelastic modeling and control of wind turbine generators. At first, we describe a finite-element-based multibody dynamics code that is used in this effort for modeling wind turbine aeroelastic systems. Next, we formulate an adaptive nonlinear model-predictive controller. The adaptive element in the formulation enables the controller to correct the deficiencies of the reduced model used for the prediction, and to self-adjust to changing operating conditions. In this work, we verify the performance of the controller when the solution of the prediction problem is obtained by means of a direct transcription approach. The tests conducted on gust response and turbulent wind operations provide some benchmark results against which to compare the performance of a real-time neural controller currently under development.

Aero-servo-elastic Modeling and Control of Wind Turbines Using Finite-Element Multibody Procedures

BOTTASSO, CARLO LUIGI;CROCE, ALESSANDRO;SAVINI, BARBARA;SIRCHI, WALTER;TRAINELLI, LORENZO
2006

Abstract

In this paper, we report on our ongoing research in the area of aeroelastic modeling and control of wind turbine generators. At first, we describe a finite-element-based multibody dynamics code that is used in this effort for modeling wind turbine aeroelastic systems. Next, we formulate an adaptive nonlinear model-predictive controller. The adaptive element in the formulation enables the controller to correct the deficiencies of the reduced model used for the prediction, and to self-adjust to changing operating conditions. In this work, we verify the performance of the controller when the solution of the prediction problem is obtained by means of a direct transcription approach. The tests conducted on gust response and turbulent wind operations provide some benchmark results against which to compare the performance of a real-time neural controller currently under development.
Multibody dynamics; Model-predictive control; Aeroelasticity; Wind energy; Neural network
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/552778
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